當使用Li-Fi作為室內的無線傳輸技術，為了照明充足會佈建大量的Li-Fi 存取點，使得處在存取點覆蓋區域重疊的使用者會受到嚴重的干擾，導致整體網路效能下降。過往的研究中使用協同式傳輸技術以及以使用者為中心的網路架構來解決此問題，在本篇論文我們透過分群的方式判斷使用者是否處在重疊區域；若處在重疊區域便採用協同式傳輸，若否則採用傳統的單一存取點傳輸。藉由依據分群結果對使用者進行傳輸模式分類增加處在重疊區域使用者的時間資源，此外對於處在重疊區域的使用者，我們透過分時多工使一個存取點可以在不同時間點服務不同的使用者，然而這會衍生存取點應該在什麼時間服務哪個使用者的排程問題，我們將此排程問題對應到彈性光網路的頻譜分配問題，透過在頻譜分配問題的解法我們設計出四種不同的排程的演算法。
透過將使用者分類我們提出的方法在系統的吞吐量上可以達到38%改善幅度，如果將使用者分類配合上妥善的排程方法則可以在系統的吞吐量上達到32%改善幅度，在實驗結果中單一存取點傳輸使用者的拒絕率表現也說明了在提升整體系統的吞吐量之目標下，我們所提出的方法並沒有犧牲太多單一存取點使用者的吞吐量。

When using Light-Fidelity (Li-Fi) for indoor wireless transmission, a large number of Li-Fi access points (APs) will be built for sufficient lighting, so that users in overlapping AP coverage areas will be severely inter-cell interference (ICI), resulting in a decrease in overall network performance. Previous research has used cooperative transmission (CT) technology and user-centric network architecture to solve this problem. In this thesis, we perform user classification after determining whether users are in overlapping areas by clustering. A UE would employ CT as downlink transmission technology if it locates in an overlapping area of multiple cells. Otherwise, the traditional single AP transmission is used to increase the time resources allocated to users in the overlapping area. In addition, it is assumed that each AP serves its associated UEs in TDMA fashion. This assumption gives rise to the problem of determining the exact time point a UE should be served by its associated APs and it is called the UE scheduling problem. Unexpectedly, we find that the UE scheduling problem is highly analogous to the spectrum allocation problem, a well-known resource allocation (SA) problem, in elastic optical networks (EONs). Applying the concepts of some existing SA algorithms, we proposed several algorithms to solve the UE scheduling problem.
By classifying users, the method we proposed can achieve a 38% improvement in terms of system capacity. With user classification and appropriate UE scheduling our proposed method is superior to previous by 32% improvement in terms of system capacity. In the simulation results, the rejection rate of a single access point transmission (SPT)　user also shows that the throughput of per SPT user is not sacrificed while increasing the overall system throughput.

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